Title |
Methods for evaluating gene expression from Affymetrix microarray datasets
|
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Published in |
BMC Bioinformatics, June 2008
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DOI | 10.1186/1471-2105-9-284 |
Pubmed ID | |
Authors |
Ning Jiang, Lindsey J Leach, Xiaohua Hu, Elena Potokina, Tianye Jia, Arnis Druka, Robbie Waugh, Michael J Kearsey, Zewei W Luo |
Abstract |
Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 3 | 2% |
United States | 2 | 1% |
France | 2 | 1% |
Portugal | 1 | <1% |
Chile | 1 | <1% |
Italy | 1 | <1% |
Germany | 1 | <1% |
Finland | 1 | <1% |
Malaysia | 1 | <1% |
Other | 2 | 1% |
Unknown | 146 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 41 | 25% |
Student > Ph. D. Student | 27 | 17% |
Professor > Associate Professor | 15 | 9% |
Student > Master | 15 | 9% |
Student > Bachelor | 14 | 9% |
Other | 31 | 19% |
Unknown | 18 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 72 | 45% |
Biochemistry, Genetics and Molecular Biology | 20 | 12% |
Computer Science | 14 | 9% |
Mathematics | 8 | 5% |
Medicine and Dentistry | 8 | 5% |
Other | 20 | 12% |
Unknown | 19 | 12% |